In a wireless communication system, a plurality of antennas is usually used to acquire a higher transmission rate by adopting a spatial multiplexing manner between a transmitting end and a receiving end. In contrast to a general spatial multiplexing method, an enhanced technical solution is mentioned in the related technology. In this solution, a receiving end feeds back channel information to a transmitting end, and the transmitting end greatly improves the transmission performance using some transmission precoding technologies according to the acquired channel information. For Single-User Multi-Input Multi-Output (MIMO for short), channel eigenvector information is directly used for precoding. While for Multi-User MIMO (MU-MIMO), more accurate channel information is needed.
In some technologies of the fourth generation (4G) mobile communication technology such as the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) technology and International Telecommunication Union (ITU) 802.16m standard specifications, channel information is mainly fed back using a relatively simple single codebook feedback method, while the performance of the MIMO transmission precoding technique is more dependent on the accuracy of the codebook feedback. The basic principle of quantization and feedback of channel information based on the codebook is briefly described as follows: assuming that a limited feedback channel capacity is Bbps/Hz, then a number of available codewords is N=2B. It is assumed that an eigenvector space of a channel matrix H is quantized to form a codebook space ={F1, F2 . . . FN}, the transmitting end and the receiving end collectively store or generate the codebook in real time (the same for the receiving end/transmitting end). The receiving end selects a code word {circumflex over (F)} which best matches the channel from the codebook space  according to the acquired channel matrix H in accordance with a certain criterion, and feeds back a codeword serial number i of the codeword {circumflex over (F)} to the transmitting end. Here, the codeword serial number is also referred to as a Precoding Matrix Indicator (PMI for short). And the transmitting end finds a corresponding precoded codeword according to the codeword serial number i which is fed back, so as to acquire the channel information, herein {circumflex over (F)} represents eigenvector information of the channel. Generally,  may be divided into codebooks corresponding to multiple ranks, and there are multiple corresponding codewords for each rank to quantize a precoding matrix formed by channel eigenvectors for the rank. In general, there may be N columns of codewords when a rank is N. Therefore, the codebook  may be divided into multiple sub-codebooks according to different ranks, as shown in table 1.
TABLE 1 A number of layers v (rank)12. . .N  1  2. . .  NA set ofA set of. . .A set ofcodeword vectorscodeword vectorscodeword vectorswhen a columnwhen a columnwhen a columnnumber is 1number is 2number is N
herein, when Rank=1, the codewords are all in a vector form, and when Rank>1, the codewords are all in a matrix form. The codebook in the LTE protocol uses the feedback method of this codebook quantization. The codebook of the LTE downlink 4-transmission antenna is shown in Table 2. In fact, the precoding codebook and the channel information quantization codebook have the same meaning in LTE. In the following, for the sake of consistency, the vector may also be seen as a matrix with a dimension of 1.
TABLE 2CodewordA total Number of layers   (RI)indexun12340u0 = [1 −1 −1 −1]TW0{1}W0{14}/{square root over (2)}W0{124}/{square root over (3)}W0{1234}/21u1 = [1 −j 1 j]TW1{1}W1{12}/{square root over (2)}W1{123}/{square root over (3)}W1{1234}/22u2 = [1 1 −1 1]TW2{1}W2{12}/{square root over (2)}W2{123}/{square root over (3)}W2{3214}/23u3 = [1 j 1 −j]TW3{1}W3{12}/{square root over (2)}W3{123}/{square root over (3)}W3{3214}/24u4 = [1 (−1 − j)/{square root over (2)} −j (1 − j)/{square root over (2)}]TW4{1}W4{14}/{square root over (2)}W4{124}/{square root over (3)}W4{1234}/25u5 = [1 (1 − j)/{square root over (2)} j (−1 − j)/{square root over (2)}]TW5{1}W5{14}/{square root over (2)}W5{124}/{square root over (3)}W5{1234}/26u6 = [1 (1 + j)/{square root over (2)} −j (−1 + j)/{square root over (2)}]TW6{1}W6{13}/{square root over (2)}W6{134}/{square root over (3)}W6{1324}/27u7 = [1 (−1 + j)/{square root over (2)} j (1 + j)/{square root over (2)}]TW7{1}W7{13}/{square root over (2)}W7{134}/{square root over (3)}W7{1324}/28u8 = [1 −1 1 1]TW8{1}W8{12}/{square root over (2)}W8{124}/{square root over (3)}W8{1234}/29u9 = [1 −j −1 −j]TW9{1}W9{14}/{square root over (2)}W9{134}/{square root over (3)}W9{1234}/210u10 = [1 1 1 −1]TW10{1}W10{13}/{square root over (2)}W10{123}/{square root over (3)}W10{1324}/211u11 = [1 j −1 j]TW11{1}W11{13}/{square root over (2)}W11{134}/{square root over (3)}W11{1324}/212u12 = [1 −1 −1 1]TW12{1}W12{12}/{square root over (2)}W12{123}/{square root over (3)}W12{1234}/213u13 = [1 −1 1 −1]TW13{1}W13{13}/{square root over (2)}W13{123}/{square root over (3)}W13{1324}/214u14 = [1 1 −1 −1]TW14{1}W14{13}/{square root over (2)}W14{123}/{square root over (3)}W14{3214}/215u15 = [1 1 1 1]TW15{1}W15{12}/{square root over (2)}W15{123}/{square root over (3)}W15{1234}/2
herein, Wn=I−2ununH/unHun, I is an identity matrix, Wk(j) represents a vector in a jth column of a matrix Wk. Wk(j1, j2, . . . jn) represents a matrix composed of j1th, j2th, . . . , jnth columns of the matrix Wk.
In the LTE-Advance technology, the codebook feedback is enhanced to a certain extent. For Rank=r, herein r is an integer, it differs from the previous 4Tx codebook in that when the codebook feedback is used, feedback of codewords in the corresponding codebook require feedback of 2 PMIs to represent their information, which can generally be expressed as shown in Table 3 below.
TABLE 3i201. . .N2i10Wi1,i2Wi1,i2Wi1,i2Wi1,i21Wi1,i2Wi1,i2Wi1,i2Wi1,i2. . .Wi1,i2Wi1,i2Wi1,i2Wi1,i2N1Wi1,i2Wi1,i2Wi1,i2Wi1,i2
Here, Wi1,i2 is a codeword commonly indicated by i1 and i2, and can usually be written as a function form W(i1,i2), and it only needs to determine i1 and i2. For example, when r=2, i1 and i2 are as shown in Table 4.
TABLE 4i2i101230-15W2i1,2i1,0(2)W2i1,2i1,1(2)W2i1+1,2i1+1,0(2)W2i1+1,2i1+1,1(2)i2i145670-15W2i1+2,2i1+2,0(2)W2i1+2,2i1+2,1(2)W2i1+3,2i1+3,0(2)W2i1+3,2i1+3,1(2)i2i18910110-15W2i1,2i1+1,0(2)W2i1,2i1+1,1(2)W2i1+1,2i1+2,0(2)W2i1+1,2i1+2,1(2)i2i1121314150-15W2i1,2i1+3,0(2)W2i1,2i1+3,1(2)W2i1+1,2i1+3,0(2)W2i1+1,2i1+3,1(2)            where      ⁢                          ⁢              W                  m          ,                      m            ′                    ,          n                          (          2          )                      =                  1        4            ⁡              [                                                            v                m                                                                    v                                  m                  ′                                                                                                                          ϕ                  n                                ⁢                                  v                  m                                                                                                      -                                      ϕ                    n                                                  ⁢                                  v                                      m                    ′                                                                                      ]              ,            ϕ      n        =          e              j        ⁢                                  ⁢        π        ⁢                                  ⁢                  n          /          2                      ,            v      m        =          [                                    1                                                                                e                                      j                    ⁢                                                                                  ⁢                    2                    ⁢                                                                                  ⁢                    π                    ⁢                                                                                  ⁢                                          m                      /                      32                                                                      ]                            T                                          φn = ejπn/2vm = [1 ej2πm/32 ej4πm/32 ej6πm/32]T
The primary difference is in that: a codeword model is defined for each supported condition of a number of layers, feedback is performed based on the codeword model, and the meaning of parameters in the model is determined through one or two PMIs which are fed back, so as to finally acquire accurate channel eigenvector information for precoding.
With the rapid development of wireless communication technology, wireless applications of users are increasingly abundant, which also bring wireless data services to grow rapidly. It is predicted that in the next 10 years, the data services will grow at 1.6-2 times the rate per year. This will undoubtedly bring unlimited opportunities and enormous challenges to wireless access networks, and the multi-antenna technology is a key technology to deal with the challenges of the explosive growth of wireless data services. Currently, the multi-antenna technology supported in 4G supports only at most 8-port horizontal dimension beamforming technology, and has a large potential to further significantly increase the system capacity.
At present, the multi-antenna technology mainly evolved in terms of purposes such as a greater beamforming/precoding gain, a larger spatial multiplexing layer number (MU/SU) and a smaller inter-layer interference, a more comprehensive coverage, a smaller inter-site interference, etc. Massive MIMO and three-dimensional MIMO (3D MIMO) are the two most important technologies for MIMO evolution in the next-generation wireless communications.
In a Massive MIMO technology-based system, a large-scale antenna array is configured at a base station side, for example, 16, 32, 64, 128, 256 antennas, or even a large number of antennas. The antennas referred to in this application document are generally understood to be defined antenna ports. In this way, multiple users are co-multiplexed at the same time using the MU-MIMO technology during data transmission. Generally speaking, a ratio between the number of antennas and the number of multiplexed users is maintained to be about 5-10 times. On the one hand, a correlation coefficient between channels of any two users exponentially decays with the increase of the number of antennas, no matter whether in a strong correlated channel in the line-of-sight environment or an unrelated channel under rich scattering. For example, when 100 or more antennas are configured at the base station side, the correlation coefficient between the channels of any two users approaches 0, that is, channels corresponding to multiple users are close to be orthogonal. On the other hand, a large array can result in very impressive array gain and diversity gain. In addition, in order to save the size of the antenna and provide better diversity performance or multiplexing capability, dual-polarized antennas are also widely used in the massive MIMO. The use of the dual-polarized antennas can reduce the size of the antennas to a half of the original size.
For the Massive MIMO, due to the requirements for the quantization accuracy in a case of the introduction of a large number of antennas and the increase of the antenna dimension, in an existing codebook feedback model, there are some defects in feedback of channel characteristic information or precoding information using a quantization model such as
      W          m      ,              m        ′            ,      n              (      2      )        =            1      4        ⁡          [                                                  v              m                                                          v                              m                ′                                                                                                        ϕ                n                            ⁢                              v                m                                                                                        -                                  ϕ                  n                                            ⁢                              v                                  m                  ′                                                                        ]      etc. as shown in Table 4, and the primary reasons for such defects are that the problem of polarization leakage which actually exists in the channel is not taken into account when the feedback model is designed. The complete isolation of ideal polarization does not exist, and a signal fed into a polarized antenna will always be fed into another polarized antenna more or less. In the electromagnetic wave propagation process, metal outside a building may often lead to polarization rotation, which will cause polarization leakage. Complex coupling characteristics exist in a non-ideal dual-polarized system, and therefore, the channel response characteristics of the dual-polarized system are also very complex. On the basis that the idealized dual-polarization channel without consideration of polarization leakage is not suitable for the Massive MIMO, the feedback method which is designed for the idealized dual-polarization channel is not very robust in the Massive MIMO. Therefore, some existing design considerations may need to be improved and enhanced in order to be suitable for the Massive MIMO with higher accuracy requirements.
It is assumed that the dual-polarized antenna system has Mt transmission antennas and Mr reception antennas, herein Mt/2 transmission antennas are polarized in a direction and other Mt/2 transmission antennas are polarized in a direction. Similarly, at the receiving end, the Mr/2 transmission antennas are polarized in a direction and other Mr/2 transmission antennas are polarized in a direction. FIG. 1 is a diagram of Mt transmission antennas and Mr reception antennas existing in a dual polarized antenna system according to the related art. As shown in FIG. 1,
a received signal y may be modeled as (1):y=√{square root over (ρ)}z*Hfs+z*n  (1)
herein z is a received weight vector in an Mr dimension of the receiving end, f is a unit-norm precoding vector of the transmitting end, n is a Gaussian white noise in an Mr dimension, which follows a distribution of CN(0,1), s is a transmitted signal, Es(ssH)≤1, and ρ represents an SNR.
A dual-polarization channel H may be modeled as (2):H=Hw·X  (2)
herein, · represents Hadamard Product of the matrixes, which is a non-correlated channel scenario, and Hw is approximately an Nr×Nt-dimensional Gaussian channel. A channel response between each pair of antennas is subject to a distribution of CN(0,1). X is a matrix related to cross-polar discrimination (XPD), and has an expression of (3), and κ represents a cross-polar ratio (XPR), which is an inverse of XPD.
                    X        =                              [                                                            1                                                                      κ                                                                                                                    κ                                                                    1                                                      ]                    ⊗                                    [                                                                    1                                                        …                                                        1                                                                                        …                                                        …                                                        …                                                                                        1                                                        …                                                        1                                                              ]                                      Mr              ×              M              ⁢                                                          ⁢              t                                                          (        3        )            
herein Mr and Mt represent reception and transmission antennas, and in the existing technology, it is assumed that √{square root over (κ)} is a model of a 0 analysis channel eigenvector. Most of the research hypotheses in the paper are that cases of making a research on a relatively ideal X. When a case without polarization leakage is considered, the eigenvectors of the channel have block diagonal characteristics, for example,
      [                                        v            1                                                v            2                                                            av            1                                                -                          av                              2                ⁢                                                                                                            ]    ,herein v1 and v2 are vectors in an Mt/2 dimension, and a is a phase parameter with a modulo value of 1. This conclusion is widely used in a feedback design. A model in table 4, i.e.,
      W          m      ,              m        ′            ,      n              (      2      )        =            1      4        ⁡          [                                                  v              m                                                          v                              m                ′                                                                                                        ϕ                n                            ⁢                              v                m                                                                                        -                                  ϕ                  n                                            ⁢                              v                                  m                  ′                                                                        ]      is acquired based on the model.
However, in practice, a case that there is completely no polarization leakage is almost not existent in the actual system, and a typical polarization leakage ratio (XPR) in the various scenarios of the 3GPP LTE is usually regulated to be concentrated in a range of −4 dB˜−12 dB, while for a typical scenario, it is generally around −8 dB, and regulations in some of the other literatures are similar, for example: [x] indicates that κ is typically −7.2 dB to −8 dB.
In a case where the number of transmission antennas is small, √{square root over (κ)} is considered approximately to be 0, which does not have significant influence on a codeword chordal distance, but with the increase of the number of transmission antennas, the influence of √{square root over (κ)} on the chordal distance will increase. By taking 32 and 64 antennas as an example, a feedback designed without considering the polarization leakage, for example, meets a codeword with a model of
            W              m        ,                  m          ′                ,        n                    (        2        )              =                  1        4            ⁡              [                                                            v                m                                                                    v                                  m                  ′                                                                                                                          ϕ                  n                                ⁢                                  v                  m                                                                                                      -                                      ϕ                    n                                                  ⁢                                  v                                      m                    ′                                                                                      ]              ,which has an upper bound of quantized feedback performance, and if it is characterized by the chordal distance, a bound of a minimum chordal distance between the codeword and the actual channel eigenvector can be solved by assuming that parameters such as vi, vjα etc. have unlimited overhead. FIG. 2 is a diagram of a CDF corresponding to 32Tx without considering the minimum quantization error (chordal distance) of the polarization leakage in the related technology. FIG. 3 is a diagram of a CDF corresponding to 64Tx without considering the minimum quantization error (chordal distance) of the polarization leakage in the related technology. As shown in FIG. 2 and FIG. 3, the polarization leakage considered in the simulation here is κ=−8 dB, i.e., √{square root over (κ)}=0.3981. It can be seen that even if polarization leakage of only −8 dB is considered, in about 70% cases, the theoretical minimum chordal distance between the codeword and the channel eigenvector is more than 0.5 for 32Tx, and in about 80% cases, the theoretical minimum chordal distance between the codeword and the channel eigenvector is more than 0.5 for 64Tx, and the chordal distance can reflect a loss of the useful signal power. Therefore, the existing dual-polarization codeword model has a bottleneck, that is, there is still a room for optimization, and in the optimized space, in many cases, a gain for the useful signal is more than 3 dB. In the actual channel environment, since XPR=−8 dB is only a relatively typical value, κ of some of the UEs in the actual system may reach −4 dB, which may have greater influences on the performance, and will seriously affect the feedback performance of these UEs, thus affecting the user data service rate. Therefore, the feedback model without considering polarization leakage will be limited by a theoretical upper limit, which will restrict the system performance.
In conclusion, the feedback model is designed without considering polarization leakage in the related technology, and the feedback based on this feedback model is not applicable to Massive MIMO because Massive MIMO is very sensitive to the quantization accuracy of channel information. Therefore, the requirements for the accuracy of the feedback model are also very high.